Neonatal apnea, or apnea of prematurity, is the most common serious disorder of premature infants in the neonatal intensive care unit (NICU). There are significant gaps in knowledge in its definition, documentation and management. For example, despite continuous electronic monitoring in the NICU, apnea episodes are diagnosed based only on clinical judgment and recorded only by hand on bedside flow sheets. The lack of quantitative and standardized apnea metrics has stalled efforts to understand its natural history, its therapy, and its relationship to the major public health problem of sudden infant death syndrome, or SIDS. Our long term goals are to change NICU practice by providing doctors and nurses with real time risk assessment for subacute, potentially catastrophic diseases. Here, our Specific Aim is to develop and validate multivariable predictive models for continuous monitoring of neonatal apnea using old and new mathematical measures of waveforms and vital signs recorded on NICU bedside monitors. We will deliver a short-term apnea index and a cumulative apnea burden as management and research tools suitable for mechanistic insights and for randomized therapeutic trials. The project is responsive to the scope and requirements of the Grand Opportunities grants program. We plan a large-scale and accelerated program to generate a large database of neonatal clinical and monitor waveform data, and to develop monitoring algorithms. The database is a rich resource for other researchers, and the algorithms will be innovative solutions to current barriers to data interpretation in NICUs, and promise improved infant health. New large scale computing hardware and software is already running. Our team has unique experience in developing new monitoring algorithms in the NICU, and represents clinical, engineering and mathematical disciplines. No other entity is likely to conceive or to execute this work, and no other funding source seems likely. PUBLIC HEALTH RELEVANCE: Apnea of prematurity (AOP) is a common life-threatening disorder that often extends expensive hospitalization of premature babies. While methods for detecting, recording, and classifying AOP currently depend on highly unreliable nursing observations, computer technology and storage capacity are now readily available to permit continuous on line analysis of existing signals from routine bedside monitors to predict apnea risk. We have the technology, expertise, and existing network to develop and verify a sensitive measure of apnea risk which could be widely implemented in other NICUs to save substantial hospital costs through earlier safe hospital discharge, as well as to contribute valuable knowledge about maturation of respiratory control and provide a useful tool and database for future studies of AOP and Sudden Infant Death Syndrome (SIDS).